Today I am going to show you how to use Adobe’s Calculated Metric functions in order to set up a dashboard and monitor when your critical metrics & KPI’s perform outside normal/predicted behaviour.

Currently Adobe Sitecatalyst gives a feature called “anomaly detection”, know to most Google analytic users as “Intelligent alerts”.

Anomaly Detection in Adobe Sitecatalyst and why you don’t really need it

Anomaly detection will spot unpredicted behavior – whether this is positive or negative – based on previous data you hold (you can read more about this here). They way it does this is by calculating the daily total for the selected metric and compare it with the training period using each of the following algorithms:

Holt Winters Multiplicative (Triple Exponential Smoothing)

Holt Winters Additive (Triple Exponential Smoothing)

Holts Trend Corrected (Double Exponential Smoothing)

Each algorithm is applied to determine the algorithm with the smallest Sum of Squared Errors (SSE). The Mean Absolute Percent Error (MAPE) and the current Standard Error are then calculated to make sure that the model is statistically valid.

90 days is pretty much insignificant for retail web sites where seasonality and trends plays a big role in terms of performance, hopefully they will add a YoY feature in future releases but in case they don;t that is ok because as long as you have the data available you can do it yourself as shown below.

Back in 2010, Adobe introduced Digital Pulse Debugger, a JS snippet that you could add to your Bookmark and act as your Sitecatalyst, Test & Target, AdLens and Audiomanager debugger. Effectively, an image requests that shows you via a basic interface all the variable and success events that fire once your page loads amongst other useful information such as Report Suite ID, Visitor ID etc.

Pulse became a must have extension, however it lacked the ability to show you a sequence of requests (you could do that with Charles), onclick events and variables that fire without a page load as well as possible errors with your variable settings within the admin console. For that you have to go via the network option in the development console or use 3rd party tools such as Observe point and Charles.

The majority of the time landing page analysis is the primary focus of any web analyst when carrying out a web site review. However, little attention is given to visitor departure pages, also known as the Exit Rate.

This lack of focus is somehow understandable, especially on heavy acquisition web sites, where CMOs and Heads of Digital spend heavy budgets on biddable media and want to know which channel performs best when it comes to their KPIs. Consequently, the focus goes to landing page optimization metrics, in order to analyse visitors. It’s important that you don’t, however, ignore your visitors and what they do next.

Most of the time this happens because unlike the Bounce Rate, there isn’t a set rule for the Exit Rate. A visitor could end the visit at any moment without a meaningful reason, thus it is harder to explain. I believe that an exit rate analysis can give you a lot of important answers as to why particular visitor segments are exiting your site prematurely.